Implement notebook mode

README.md

@@ -72,6 +72,7 @@ Then browse to
 Optionally, you can use the following command-line flags:
 
     --model model-name: load this model by default.
+    --notebook: Launch the webui in notebook mode, where the output is written to the same text box as the input.
 
 ## Presets
 

server.py

@@ -11,7 +11,8 @@ from transformers import AutoTokenizer
 from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--model', type=str, help='Name of the model to load by default')
+parser.add_argument('--model', type=str, help='Name of the model to load by default.')
+parser.add_argument('--notebook', action='store_true', help='Launch the webui in notebook mode, where the output is written to the same text box as the input.')
 args = parser.parse_args()
 loaded_preset = None
 available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*[!\.][!t][!x][!t]")+ glob.glob("torch-dumps/*[!\.][!t][!x][!t]"))))
@@ -79,7 +80,10 @@ def generate_reply(question, temperature, max_length, inference_settings, select
     if model_name.startswith('gpt4chan'):
         reply = fix_gpt4chan(reply)
 
-    return reply
+    if model_name.lower().startswith('galactica'):
+        return reply, reply
+    else:
+        return reply, ''
 
 # Choosing the default model
 if args.model is not None:
@@ -104,6 +108,25 @@ if model_name.startswith('gpt4chan'):
 else:
     default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
 
+if args.notebook:
+    with gr.Blocks() as interface:
+        gr.Markdown(
+        f"""
+        # Text generation lab
+        Generate text using Large Language Models.
+        """
+        )
+
+        textbox = gr.Textbox(value=default_text, lines=23)
+        temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
+        length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
+        preset_menu = gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="Default")
+        model_menu = gr.Dropdown(choices=available_models, value=model_name)
+        btn = gr.Button("Generate")
+        markdown = gr.Markdown()
+
+        btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown], show_progress=False)
+else:
     interface = gr.Interface(
         generate_reply,
         inputs=[
@@ -115,6 +138,7 @@ interface = gr.Interface(
         ],
         outputs=[
              gr.Textbox(placeholder="", lines=15),
+             gr.Markdown()
         ],
         title="Text generation lab",
         description=f"Generate text using Large Language Models.",